January 2022 arXiv papers — page 99

Ahmed Abdeljawad

In this work, we examine the approximation capabilities of deep neural networks utilizing the Rectified Quadratic Unit (ReQU) activation function, defined as \(\max(0,x)^2\), for approximating H\"older-regular functions with respect to the uniform norm. We constructively prove that deep neural networks with ReQU activation can approximate any function within

Shunli Wang, Dingkang Yang, Peng Zhai, Chixiao Chen

In recent years, assessing action quality from videos has attracted growing attention in computer vision community and human computer interaction. Most existing approaches usually tackle this problem by directly migrating the model from action recognition tasks, which ignores the intrinsic differences within the feature map such as foreground and background

Nelvin Tan, Way Tan, Jonathan Scarlett

In the group testing problem, the goal is to identify a subset of defective items within a larger set of items based on tests whose outcomes indicate whether any defective item is present. This problem is relevant in areas such as medical testing, DNA sequencing, and communications. In this paper, we study a doubly-regular design in which the number of tests

A. J. Goodwin, S. van Velzen, J. C. A. Miller-Jones, A. Mummery

Tidal disruption events (TDEs) occur when a star is destroyed by a supermassive black hole at the center of a galaxy, temporarily increasing the accretion rate onto the black hole and producing a bright flare across the electromagnetic spectrum. Radio observations of TDEs trace outflows and jets that may be produced. Radio detections of the outflows from TDE

Hiroki Fukuda, Akira Nagakubo, Hirotsugu Ogi

Elastic constants and sound velocities of nm-order thin films are essential for designing acoustic filters. However, it is difficult to measure them for dielectric thin films. In this study, we use a three-layer structure where a dielectric nano-thin film is sandwiched between thicker metallic films to measure the longitudinal elastic constant of the dielect

Hanjie Chen, Wanyu Du, Yangfeng Ji

Predictive uncertainty estimation of pre-trained language models is an important measure of how likely people can trust their predictions. However, little is known about what makes a model prediction uncertain. Explaining predictive uncertainty is an important complement to explaining prediction labels in helping users understand model decision making and ga

Isabel Byrne, Natalie Dodson, Ryan Lynch, Eric Pabón

In this article we prove that a class of Goppa codes whose Goppa polynomial is of the form $g(x) = x + x^q + \cdots + x^{q^{m-1}}$ where $m \geq 3$ (i.e. $g(x)$ is a trace polynomial from a field extension of degree $m \geq 3$) has a better minimum distance than what the Goppa bound $d \geq 2deg(g(x))+1$ implies. Our improvement is based on finding another G

Sneha Gathani, Shayan Monadjemi, Alvitta Ottley, Leilani Battle

Researchers collect large amounts of user interaction data with the goal of mapping user's workflows and behaviors to their higher-level motivations, intuitions, and goals. Although the visual analytics community has proposed numerous taxonomies to facilitate this mapping process, no formal methods exist for systematically applying these existing theories to

Prabesh Bajracharya, Vinay Sharma, Anthony Johnson, Ramesh C. Budhani

Measurements of frequency dependent ferromagnetic resonance (FMR) and spin pumping driven dc voltage (V_{dc}) are reported for amorphous films of Fe_{78}Ga_{13}B_{9} (FeGaB) alloy to address the phenomenon of self-induced inverse spin Hall effect (ISHE) in plain films of metallic ferromagnets. The V_{dc} signal, which is antisymmetric on field reversal, comp

Peng Song, Haodong Ma, Rui Wang, Baigeng Wang

The conformal field theory (CFT) approach to Kondo problems, originally developed by Affleck and Ludwig (AL), has greatly advanced the fundamental knowledge of Kondo physics. The CFT approach to Kondo impurities is based on a necessary approximation, i.e., the linearization of the low-lying excitations in a narrow energy window about the Fermi surface. This

Andrew Charles Brady

There exist few examples of negative Latin square type partial difference sets (NLST PDSs) in nonabelian groups. We present a list of 176 inequivalent NLST PDSs in 48 nonisomorphic, nonabelian groups of order 64. These NLST PDSs form 8 nonisomorphic strongly regular graphs. These PDSs were constructed using a combination of theoretical techniques and compute

Shin Sano, Seiji Yamada

We present an AI-assisted search tool, the "Design Concept Exploration Graph" ("D-Graph"). It assists automotive designers in creating an original design-concept phrase, that is, a combination of two adjectives that conveys product aesthetics. D-Graph retrieves adjectives from a ConceptNet knowledge graph as nodes and visualizes them in a dynamically scalabl

Daniel Gibney, Jan-Niklas Boyn, David A. Mazziotti

Density functional theory (DFT), the most widely adopted method in modern computational chemistry, fails to describe accurately the electronic structure of strongly correlated systems. Here we show that DFT can be formally and practically transformed into a one-electron reduced-density-matrix (1-RDM) functional theory, which can address the limitations of DF

Arpan Sadhukhan

This paper proves the existence of nonmeasurable dense sets with additional properties using combinatorial techniques.

Gaoxiong Zeng

Applications running in geographically distributed setting are becoming prevalent. Large-scale online services often share or replicate their data into multiple data centers (DCs) in different geographic regions. Driven by the data communication need of these applications, inter-datacenter network (IDN) is getting increasingly important. However, we find con

Kenneth Duru, Gunilla Kreiss

It is well-known that reliable and efficient domain truncation is crucial to accurate numerical solution of most wave propagation problems. The perfectly matched layer (PML) is a method which, when stable, can provide a domain truncation scheme which is convergent with increasing layer width/damping. The difficulties in using the PML are primarily associated

Miran Jeong, Jinmi Hwang, Sejong Kim

A new quantum divergence induced from the $\alpha-z$ Renyi relative entropy, called the $\alpha-z$ Bures-Wasserstein quantum divergence, has been recently introduced. We investigate in this paper properties of the right mean, which is a unique minimizer of the weighted sum of $\alpha-z$ Bures-Wasserstein quantum divergences to each points. Many interesting o

Alva Kosasih, Vincent Onasis, Wibowo Hardjawana, Vera Miloslavskaya

Multiuser massive multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. In an uplink MUMIMO system, a base station is serving a large number of users, leading to a strong multi-user interference (MUI). Designing a high performance detector in the presence of a strong MUI is a challenging

Sibei Yang, Dachun Yang

Let $n\ge2$ and $\Omega$ be a bounded non-tangentially accessible domain (for short, NTA domain) of $\mathbb{R}^n$. Assume that $L_D$ is a second-order divergence form elliptic operator having real-valued, bounded, measurable coefficients on $L^2(\Omega)$ with the Dirichlet boundary condition. The main aim of this article is threefold. First, the authors pro

Golsa Mirbagheri, David T. Crouse

Tunable dielectric meta-surface nanostructures offer incredible performance in optical application due to their extraordinary tunability of the polarization and engineering the dispersion of light with low loss in infrared range. In this article, we designed and experimentally measured the tunability of all-dielectric subwavelength silicon nanoparticles with

Sai Li, T. Tony Cai, Hongzhe Li

Existing high-dimensional statistical methods are largely established for analyzing individual-level data. In this work, we study estimation and inference for high-dimensional linear models where we only observe "proxy data", which include the marginal statistics and sample covariance matrix that are computed based on different sets of individuals. We develo

Joshua A. Rackers, Lucas Tecot, Mario Geiger, Tess E. Smidt

A long-standing goal of science is to accurately solve the Schr\"odinger equation for large molecular systems. The poor scaling of current quantum chemistry algorithms on classical computers imposes an effective limit of about a few dozen atoms for which we can calculate molecular electronic structure. We present a machine learning (ML) method to break throu

Golsa Mirbagheri, David T. Crouse

Hyperbolic Metamaterials, as a non-magnetic anisotropic artificial structure, show metal properties in one direction and dielectric behavior in orthogonal directions. The proposed hyperbolic metamaterial filter in this project is designed with the metal wire mesh perpendicular to the alternative layers of dielectric materials, keeps TM center wavelength unch

Oscar Perdomo, Nelson Castaneda, Roger Vogeler

We will call a pure qubit state real if all its amplitudes are real numbers. We show that any real 3-qubit state can be prepared using $R_y(\theta)$ gates and at most four controlled-$Z$ gates, and we conjecture that four is optimal. We also present an algorithm -- different from the 2008 algorithm given by Znidaric, Giraud and Georgeot -- that prepares any

Esha Kundu

Fast radio burst (FRB) 191001 is localised at the spiral arm of a highly star-forming galaxy with an observed dispersion measure (DM) of 507 pc cm$^{-3}$. Subtracting the contributions of the intergalactic medium and our Milky Way Galaxy from the total DM, one gets an excess of around 200 pc cm$^{-3}$, which may have been contributed by the host galaxy of th

Golsa Mirbagheri, David T. Crouse, Chee-Keong Tan

Vanadium dioxide (VO2) as a phase-change material controls the transferred heat during phase transition process between metal and insulator states. At temperature above 68C, the rutile structure VO2 keeps the heat out and increases the IR radiation reflectivity, while at the lower temperature the monoclinic structure VO2 acts as the transparent material and

Min Dai, Xiaodian Chen, Kun Wang, Yangping Luo

The development of large-scale time-domain surveys provides an opportunity to study the physical properties as well as the evolutionary scenario of B-type subdwarfs (sdB) and M-type dwarfs (dM). Here, we obtained 33 sdB+dM eclipsing binaries based on the Zwicky Transient Facility (ZTF) light curves and $Gaia$ early data release 3 (EDR3) parallaxes. By using

Natraj Raman, Sameena Shah, Manuela Veloso

Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text. The inclusion of structural relationship between documents can benefit the retrieval mechanism by addressing semantic gaps. However, incorporating these relationships requires tractable mechanisms that balance structure with

Chao Liu, Tarik Tosun, Mark Yim

Accurate position sensing is important for state estimation and control in robotics. Reliable and accurate position sensors are usually expensive and difficult to customize. Incorporating them into systems that have very tight volume constraints such as modular robots are particularly difficult. PaintPots are low-cost, reliable, and highly customizable posit

Guang Yang, Médéric Boquien, William N. Brandt, Véronique Buat

Modern and future surveys effectively provide a panchromatic view for large numbers of extragalactic objects. Consistently modeling these multiwavelength survey data is a critical but challenging task for extragalactic studies. The Code Investigating GALaxy Emission (CIGALE) is an efficient PYTHON code for spectral energy distribution (SED) fitting of galaxi

Marcos Escobar-Anel, Matt Davison, Yichen Zhu

This paper challenges the use of stocks in portfolio construction, instead we demonstrate that Asian derivatives, straddles, or baskets could be more convenient substitutes. Our results are obtained under the assumptions of the Black--Scholes--Merton setting, uncovering a hidden benefit of derivatives that complements their well-known gains for hedging, risk

Rozhin Yousefjani, Sougato Bose, Abolfazl Bayat

The fate of many-body localization in long-range interacting systems is not fully settled. For instance, the phase boundary between ergodic and many-body localized regimes is still under debate. Here, we use Floquet dynamics which can induce many-body localization in a clean long-range interacting system through spatiotemporal disorder, which are realized by

Lauren Partin, Daniele E. Schiavazzi, Carlos A. Sing Long

Novel Magnetic Resonance (MR) imaging modalities can quantify hemodynamics but require long acquisition times, precluding its widespread use for early diagnosis of cardiovascular disease. To reduce the acquisition times, reconstruction methods from undersampled measurements are routinely used, that leverage representations designed to increase image compress

F. Georges, M. N. H. Rashad, A. Stefanko, M. Dlamini

We report high-precision measurements of the Deeply Virtual Compton Scattering (DVCS) cross section at high values of the Bjorken variable $x_B$. DVCS is sensitive to the Generalized Parton Distributions of the nucleon, which provide a three-dimensional description of its internal constituents. Using the exact analytic expression of the DVCS cross section fo

Ye Jia, Michelle Tadmor Ramanovich, Quan Wang, Heiga Zen

We introduce CVSS, a massively multilingual-to-English speech-to-speech translation (S2ST) corpus, covering sentence-level parallel S2ST pairs from 21 languages into English. CVSS is derived from the Common Voice speech corpus and the CoVoST 2 speech-to-text translation (ST) corpus, by synthesizing the translation text from CoVoST 2 into speech using state-o

Mariia Demianenko, Kirill Grishin, Victoria Toptun, Igor Chilingarian

Large time-domain surveys provide a unique opportunity to detect and explore variability of millions of sources on timescales from days to years. Broadband photometric variability can be used as the key selection criteria for weak type-I active galactic nuclei (AGN), when other "direct" confirmation criteria like X-ray or radio emission are unavailable. Howe

Zhaohui Wang, Xiao Lin, Abhinav Mishra, Ram Sriharsha

Changepoints are abrupt variations in the underlying distribution of data. Detecting changes in a data stream is an important problem with many applications. In this paper, we are interested in changepoint detection algorithms which operate in an online setting in the sense that both its storage requirements and worst-case computational complexity per observ

Andrew Butcher, Michael Bradley Johanson, Elnaz Davoodi, Dylan J. A. Brenneis

In this paper, we contribute a multi-faceted study into Pavlovian signalling -- a process by which learned, temporally extended predictions made by one agent inform decision-making by another agent. Signalling is intimately connected to time and timing. In service of generating and receiving signals, humans and other animals are known to represent time, dete

Mengxi Yang, Dai Shi, Xuebin Zheng, Jie Yin

This paper aims to provide a novel design of a multiscale framelet convolution for spectral graph neural networks (GNNs). While current spectral methods excel in various graph learning tasks, they often lack the flexibility to adapt to noisy, incomplete, or perturbed graph signals, making them fragile in such conditions. Our newly proposed framelet convoluti

Naoki Hori, Marco Edoardo Rosti, Shu Takagi

We describe an immersed boundary method in which the fluid-structure coupling is achieved in an Eulerian framework. The method is an improved extension of the immersed boundary method originally developed by Kajishima et al. [1], which accounts for the inertia of the fictitious fluid inside the particle volume and is thus able to reproduce the behaviour of p

Peter Harremoës

Rate distortion theory was developed for optimizing lossy compression of data, but it also has a lot of applications in statistics. In this paper we will see how rate distortion theory can be used to analyze a complicated data set involving orientations of early Islamic mosques. The analysis involves testing, identification of outliers, choice of compression

Qiao Huang, Jean-Claude Zambrini

Classical geometric mechanics, including the study of symmetries, Lagrangian and Hamiltonian mechanics, and the Hamilton-Jacobi theory, are founded on geometric structures such as jets, symplectic and contact ones. In this paper, we shall use a partly forgotten framework of second-order (or stochastic) differential geometry, developed originally by L. Schwar

Stefano Olivieri, Mattia Cavaiola, Andrea Mazzino, Marco Edoardo Rosti

The spreading of the virus-containing droplets exhaled during respiratory events, e.g., cough, is an issue of paramount importance for the prevention of many infections such as COVID-19. According to the scientific literature, remarkable differences can be ascribed to several parameters that govern such complex and multiphysical problem. Among these, a parti

Lin Weng

In this paper, we develop some basic techniques towards the Riemann hypothesis for higher rank non-abelian zeta functions of an integral regular projective curve of genus $g$ over a finite field $\mathbb F_q$. As an application of the Riemann hypothesis for these genuine zeta functions, we obtain some explicit bounds on the fundamental non-abelian $\alpha$-

Maki Nakasuji, Wataru Takeda

We discuss the shuffle product of the Schur multiple zeta values, which are the special values of Schur multiple zeta functions. We first define $2$-labeled Schur posets to generalize Yamamoto's integral expression of the multiple zeta values and consider the product of hook-type Schur multiple zeta values by using these posets. Then, for the derived ter

Reto Buzano, Gianmichele Di Matteo

We develop a refined singularity analysis for the Ricci flow by investigating curvature blow-up rates locally. We first introduce general definitions of Type I and Type II singular points and show that these are indeed the only possible types of singular points. In particular, near any singular point the Riemannian curvature tensor has to blow up at least at

Marco Maronese, Claudio Destri, Enrico Prati

The field of artificial neural networks is expected to strongly benefit from recent developments of quantum computers. In particular, quantum machine learning, a class of quantum algorithms which exploit qubits for creating trainable neural networks, will provide more power to solve problems such as pattern recognition, clustering and machine learning in gen

Matti Lassas, Lauri Oksanen, Suman Kumar Sahoo, Mikko Salo

We consider an inverse problem of recovering the unknown coefficients $\beta(t,x)$ and $V(t,x)$ appearing in a time-dependent nonlinear Schr\"odinger equation $ (\mathrm{i} \partial_t +\Delta +V)u + \beta u^2=0$ in $(0,T) \times M$, on Euclidean geometry as well as on Riemannian geometry. We consider measurements in $\Omega \subset M$ that is a neighborhood

Edoardo Bacci, David Parker

Deep reinforcement learning is an increasingly popular technique for synthesising policies to control an agent's interaction with its environment. There is also growing interest in formally verifying that such policies are correct and execute safely. Progress has been made in this area by building on existing work for verification of deep neural networks and

Darryl Z. Seligman, Leslie A. Rogers, Adina D. Feinstein, Mark R. Krumholz

All stars produce explosive surface events such as flares and coronal mass ejections. These events are driven by the release of energy stored in coronal magnetic fields, generated by the stellar dynamo. However, it remains unclear if the energy deposition in the magnetic fields is driven by direct or alternating currents. Recently, we presented observational

Fanchao Meng, Mark Orr, Samarth Swarup

In classic graph signal processing, given a real-valued graph signal, its graph Fourier transform is typically defined as the series of inner products between the signal and each eigenvector of the graph Laplacian. Unfortunately, this definition is not mathematically valid in the cases of vector-valued graph signals which however are typical operands in the

Rebecca Bell, Allison N. Eckert, Ryan M. Pesak, Avery Schweitzer

This paper explores topological complexity in the finite equivariant setting. We first define and study an equivariant version of Tanaka's combinatorial complexity for finite topological spaces. We explore the relationships between this invariant and several others already discussed in the literature: Farber's topological complexity, Tanaka's combinatorial c

Stefan Reissl, Paul Meehan, Ralf S. Klessen

Context: Aligned dust grains are commonly exploited to probe the magnetic field orientation. However, the exact physical processes that result in a coherent large-scale grain alignment are far from being constrained. Aims: In this work, we aim to investigate the impact of a gas-dust drift leading to a mechanical alignment of dust (MAD) and to dust polarizati

T. Koide

The systematic expansion method of the solution of the Fokker-Planck equation is developed by generalizing the formulation proposed in [J. Phys. A50, 325001 (2017)]. Using this method, we obtain a new formula to calculate the mean work perturbatively which is applicable to systems with degeneracy in the eigenvalues of the Fokker-Planck operator. This method

Duan-Cheng Liu, Pei-Yun Li, Tian-Xiang Zhu, Liang Zheng

Quantum memories at telecom wavelengths are crucial for the construction of large-scale quantum networks based on existing fiber networks. On-demand storage of telecom photonic qubits is a fundamental request for such networking applications but yet to be demonstrated. Here we demonstrate the storage and on-demand retrieval of telecom photonic qubits using a

Tian-Xiang Zhu, Chao Liu, Ming Jin, Ming-Xu Su

Photonic polarization qubits are widely used in quantum computation and quantum communication due to the robustness in transmission and the easy qubit manipulation. An integrated quantum memory for polarization qubits is a fundamental building block for large-scale integrated quantum networks. However, on-demand storing polarization qubits in an integrated q

Yajaira Concha-Sánchez, Erik Díaz-Bautista, Alfredo Raya

In this work, we construct the exact propagator for Dirac fermions in graphene-like systems immersed in external static magnetic fields with non-trivial spatial dependence. Such field profiles are generated within a first-order supersymmetric framework departing from much simpler (seed) magnetic field examples. The propagator is spanned on the basis of the R

Mustafa Kandemir, Emrah Tiras, Vincent Fischer

NuSD: Neutrino Segmented Detector is a Geant4-based user application that simulates inverse beta decay event in a variety of segmented scintillation detectors developed by different international collaborations. This simulation framework uses a combination of cross-programs and libraries including Geant4, ROOT and CLHEP developed and used by high energy phys

Danalie Azofeifa, Miguel Angel Moreles, Federico Angel Velazquez-Muñoz

Element Method. The Finite Volume Method guarantees local and global mass conservation. A property not satisfied by the Finite Volume Method. On the down side, the Finite Volume Method requires non trivial modifications to attain high order approximations unlike the Finite Volume Method. It has been contended that the Discontinuous Galerkin Method, locally c

Andreas Pöschl, Alisa Danilenko, Deividas Sabonis, Kaur Kristjuhan

We investigate local and nonlocal signatures of hybridization between a quantum dot state and an extended Andreev bound state (ABS) in a gate-defined InAs nanowire with multiple side probes. When a quantum dot in one of the side probes was hybridized with an ABS in the nanowire, a characteristic spectroscopic pattern was observed both locally, i.e., in the p

Ming Lu, Leyuan Fang, Muxing Li, Bob Zhang

The use of deep learning for water extraction requires precise pixel-level labels. However, it is very difficult to label high-resolution remote sensing images at the pixel level. Therefore, we study how to utilize point labels to extract water bodies and propose a novel method called the neighbor feature aggregation network (NFANet). Compared with pixelleve

Naoki Seto

The space gravitational wave detector LISA is expected to detect $\sim10^4$ of nearly monochromatic binaries, after $\sim 10$\.yr operation. We propose to measure the inspiral/outspiral binary fluxes in the frequency space, by processing tiny frequency drifts of these numerous binaries. Rich astrophysical information is encoded in the frequency dependencies

Craig Copi, Glenn D. Starkman

Observations of a merging neutron star binary in both gravitational waves, by the Laser Interferometer Gravitational-wave Observatory (LIGO), and across the spectrum of electromagnetic radiation, by myriad telescopes, have been used to show that gravitational waves travel in vacuum at a speed that is indistinguishable from that of light to within one part in

James W. Gardner, Hannah Middleton, Changrong Liu, Andrew Melatos

Gravitational-wave observatories around the world are searching for continuous waves: persistent signals from sources such as spinning neutron stars. These searches use sophisticated statistical techniques to look for weak signals in noisy data. In this paper, we demonstrate these techniques using a table-top model gravitational-wave detector: a Michelson in

Etienne Bonnafoux

We prove that the earthquake flow is at most polynomially mixing with a degree bounded by a constant depending only on the topology of the surface. In particular it is not exponentially mixing.

Ali R. Hadjesfandiari

In this paper, the concept of moment and couple in mechanics is examined from a fundamental perspective. It turns out that although representing a couple by its moment vector is very useful in rigid body mechanics and strength of materials, it has been very misleading in continuum mechanics. To specify the effect of a concentrated couple in continuum mechani

Donald Loveland, Jiayi Pan, Aaresh Farrokh Bhathena, Yiyang Lu

Graph Neural Networks (GNNs) have proven to excel in predictive modeling tasks where the underlying data is a graph. However, as GNNs are extensively used in human-centered applications, the issue of fairness has arisen. While edge deletion is a common method used to promote fairness in GNNs, it fails to consider when data is inherently missing fair connecti

Weiming Hu, Guido Cervone, Matteo Turilli, Andre Merzky

This chapter proposes and provides an in-depth discussion of a scalable solution for running ensemble simulation for solar energy production. Generating a forecast ensemble is computationally expensive. But with the help of Analog Ensemble, forecast ensembles can be generated with a single deterministic run of a weather forecast model. Weather ensembles are

A. P. Santos, Ishan Srivastava, Leonardo E. Silbert, Jeremy B. Lechman

The flow of frictionless granular particles is studied with stress-controlled discrete element modeling simulations for systems varying in size from 300 to 100,000 particles. The volume fraction and shear stress ratio $\mu$ are relatively insensitive to system size fo a wide range of inertial numbers $I$. Second-order effects in strain rate, such as second n

Roya Kabiri, Simin Karimi, Mihai Surdeanu

This paper presents the phonological, morphological, and syntactic distinctions between formal and informal Persian, showing that these two variants have fundamental differences that cannot be attributed solely to pronunciation discrepancies. Given that informal Persian exhibits particular characteristics, any computational model trained on formal Persian is

Evangelos Papapetros

The $w^{\star}$-rigged modules over dual operator algebras were introduced by Blecher and Kashyap as a generalization of $W^{\star}$-modules. In this paper, we introduce two new types of Morita equivalence between right $w^{\star}$-rigged modules over unital dual operator algebras and we examine whether these notions imply stable isomorphism between the corr

Sylvain Lugeon, Tiziano Piccardi, Robert West

Currently, publicly available models for website classification do not offer an embedding method and have limited support for languages beyond English. We release a dataset of more than two million category-labeled websites in 92 languages collected from Curlie, the largest multilingual human-edited Web directory. The dataset contains 14 website categories a

Ian Adelstein, Franco Vargas Pallete

It is known that Blaschke manifolds (where injectivity radius equals diameter) are Besse manifolds (where all geodesics are closed). We show that Besse manifolds with sufficiently many diameter realizing directions are Blaschke. We also provide bounds in terms of diameter on the length of the shortest closed geodesic for pinched curvature metrics on simply c

Alan D. Kaplan, Uttara Tipnis, Jean C. Beckham, Nathan A. Kimbrel

Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. We present an unsupervised probabilistic model that captures nonlinear relationships between

Joshua J. Engelsma, Steven A. Grosz, Anil K. Jain

A major impediment to researchers working in the area of fingerprint recognition is the lack of publicly available, large-scale, fingerprint datasets. The publicly available datasets that do exist contain very few identities and impressions per finger. This limits research on a number of topics, including e.g., using deep networks to learn fixed length finge

Jani Jesenovec, Benjamin L. Dutton, Nicholas Stone-Weiss, Adrian Chmielewski

In this work, bulk Czochralski-grown single crystals of 10 mol. % Al2O3 alloyed B-Ga2O3 - monoclinic 10% AGO or B-(Al0.1Ga0.9)2O3 - are obtained, which show +0.20 eV increase in the bandgap compared with unintentionally doped B-Ga2O3. Further, growths of 33% AGO - B-(Al0.33Ga0.67)2O3 - and 50% AGO - B-(Al0.5Ga0.5)2O3 or B-AlGaO3 - produce polycrystalline sin

Jie Li, Ruqian Wu

Using the density functional theory calculations, we studied the ferroelectric, electrical and optical properties of series of bias-controllable endohedral fullerenes (TM@C28, TM= Ti-Ni). The important finding is that Cr@C28 has a large electric dipole moment and an appropriate bandgap (~1.2eV) as building blocks for ferroelectric-photovoltaic materials. By

Christopher Safranski, Jonathan Z. Sun, Andrew D. Kent

Spin currents are used to write information in magnetic random access memory (MRAM) devices by switching the magnetization direction of one of the ferromagnetic electrodes of a magnetic tunnel junction (MTJ) nanopillar. Different physical mechanisms of conversion of charge current to spin current can be used in 2-terminal and 3-terminal device geometries. In

Susan E. Mullally, G. C. Sloan, J. J. Hermes, Kelly Hambleton

We use data from the Transiting Exoplanet Survey Satellite (TESS) to search for, and set limits on, optical to near-infrared photometric variability of the well-vetted, candidate James Webb Space Telescope (JWST) spectrophotometric standards. Our search of 37 of these candidate standards has revealed measurable periodic variability in 15 stars. The majority

Yoav Alon, Huiyu Zhou

Modern histopathological image analysis relies on the segmentation of cell structures to derive quantitative metrics required in biomedical research and clinical diagnostics. State-of-the-art deep learning approaches predominantly apply convolutional layers in segmentation and are typically highly customized for a specific experimental configuration; often u

Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon, Madeleine Udell

Learning invariant representations is an important requirement when training machine learning models that are driven by spurious correlations in the datasets. These spurious correlations, between input samples and the target labels, wrongly direct the neural network predictions resulting in poor performance on certain groups, especially the minority groups.

Taiane Schaedler Prass, Jonas Hendler Carlos, Cleiton Guolo Taufemback, Guilherme Pumi

In this paper we discuss dynamic ARMA-type regression models for time series taking values in $(0,\infty)$. In the proposed model, the conditional mean is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and link functions. We introduce the new class of models and discuss partial m

Kyle Broder, Kai Tang

We show that Hermitian metrics with vanishing holomorphic curvature on compact complex manifolds with pseudoeffective canonical bundle are conformally balanced. Pluriclosed metrics with vanishing holomorphic curvature on compact K\"ahler manifolds are shown to be K\"ahler and hence, are completely classified. We prove that Hermitian metrics with vanishing re

Jasmina Nasufi

In light of recent tension between theory and experiment for the process ttW in the 3l channel, we present a phenomenological study of carefully chosen observables. We employ the full off-shell results at NLO QCD for ttW+ and ttW- to build the cross section ratio, an observable which is expected to exhibit enhanced perturbative stability. Furthermore, we als

Szymon Talaga, Andrzej Nowak

The principle of similarity, or homophily, is often used to explain patterns observed in complex networks such as transitivity and the abundance of triangles (3-cycles). However, many phenomena from division of labor to protein-protein interactions (PPI) are driven by complementarity (differences and synergy). Here we show that the principle of complementari

Shaghayegh Zamani Ashtiani, Mujeeb R. Malik, Hessam Babaee

Large-scale simulations of time-dependent problems generate a massive amount of data and with the explosive increase in computational resources the size of the data generated by these simulations has increased significantly. This has imposed severe limitations on the amount of data that can be stored and has elevated the issue of input/output (I/O) into one

Dániel T. Nagy, Kartal Nagy

In extremal set theory our usual goal is to find the maximal size of a family of subsets of an $n$-element set satisfying a condition. A condition is called chain-dependent, if it is satisfied for a family if and only if it is satisfied for its intersections with the $n!$ full chains. We introduce a method to handle problems with such conditions, then show h

Jing Ma, Ruocheng Guo, Mengting Wan, Longqi Yang

Fair machine learning aims to mitigate the biases of model predictions against certain subpopulations regarding sensitive attributes such as race and gender. Among the many existing fairness notions, counterfactual fairness measures the model fairness from a causal perspective by comparing the predictions of each individual from the original data and the cou

Dani Sprague, Connor Culhane, Marina Kounkel, Richard Olney

We train a convolutional neural network, APOGEE Net, to predict $T_\mathrm{eff}$, $\log g$, and, for some stars, [Fe/H], based on the APOGEE spectra. This is the first pipeline adapted for these data that is capable of estimating these parameters in a self-consistent manner not only for low mass stars, (such as main sequence dwarfs, pre-main sequence stars,

Tristan C. Collins, Daniel Jafferis, Cumrun Vafa, Kai Xu

We consider CFT's arising from branes probing singularities of internal manifolds. We focus on holographic models with internal space including arbtirary Sasaki-Einstein manifolds coming from CY as well as arbitrary sphere quotients. In all these cases we show that there is a universal upper bound (depending only on the spacetime dimension) for the conformal

Ginette Lafit, Francisco Nogales, Marcelo Ruiz, Ruben Zamar

We propose the use of a robust covariance estimator based on multivariate Winsorization in the context of the Tarr-Muller-Weber framework for sparse estimation of the precision matrix of a Gaussian graphical model. Likewise Croux-Ollerer's precision matrix estimator, our proposed estimator attains the maximum finite sample breakdown point of 0.5 under cellwi

Felipe Lepe, Gonzalo Rivera, Jesús Vellojín

In this paper we analyze a posteriori error estimates for a mixed formulation of the linear elasticity eigenvalue problem. A posteriori estimators for the nearly and perfectly compressible elasticity spectral problems are proposed. With a post-process argument, we are able to prove reliability and efficiency for the proposed estimators. The numerical method

Masahiro Kojima

We propose simple formulas of confidence intervals for the Wald statistic, likelihood ratio statistic, and score statistic for a network meta-analysis. In addition, we consider resolutions for concerns that network meta-analyses with a small number of studies cannot hold a nominal confidence level. For a bias adjustment in analyses with a small number of stu

Federico Celi, Fabio Pasqualetti

Studying structural properties of linear dynamical systems through invariant subspaces is one of the key contributions of the geometric approach to system theory. In general, a model of the dynamics is required in order to compute the invariant subspaces of interest. In this paper we overcome this limitation by finding data-driven formulas for some of the fo

Chhavi Choudhury, Ankur Gandhe, Xiaohan Ding, Ivan Bulyko

End-to-end (E2E) automatic speech recognition models like Recurrent Neural Networks Transducer (RNN-T) are becoming a popular choice for streaming ASR applications like voice assistants. While E2E models are very effective at learning representation of the training data they are trained on, their accuracy on unseen domains remains a challenging problem. Addi

Arend Bayer, Emanuele Macrì

We survey applications of Bridgeland stability conditions in algebraic geometry and discuss open questions for future research.

Christopher Spalding, Joshua N. Winn

Stars with hot Jupiters sometimes have high obliquities, which are possible relics of hot Jupiter formation. Based on the characteristics of systems with and without high obliquities, it is suspected that obliquities are tidally damped when the star has a thick convective envelope, as is the case for main-sequence stars cooler than ~6100K, and the orbit is w

Andrei Dukov

We consider the multiplicity of limit cycles that appear when a hyperbolic polycycle is perturbed. We prove, in particular, that if such unfolding happens in generic finite-parameter families, the multiplicity of every new limit cycle does not exceed the number of separatrix connections in the polycycle.

Khang Hoang

Rare-earth (RE) doped GaN is of interest for optoelectronics and spintronics and potentially for quantum applications. A fundamental understanding of the interaction between RE dopants and the semiconductor host is key to realizing the material's full potential. This work reports an investigation of lanthanide ($Ln$) defects in GaN using hybrid density-funct

Pierre-Antoine Bernard, Nicolas Crampe, Luc Vinet

Time and band limiting operators are expressed as functions of the confluent Heun operator arising in the spheroidal wave equation. Explicit formulas are obtained when the bandwidth parameter is either small or large and results on the complete Fourier transform are recovered.